Image adjustments based on depth of field estimations

ABSTRACT

Techniques are described for automated analysis and filtering of image data. Image data is analyzed to identify regions of interest (ROIs) within the image content. The image data also may have depth estimates applied to content therein. One or more of the ROIs may be designated to possess a base depth, representing a depth of image content against which depths of other content may be compared. Moreover, the depth of the image content within a spatial area of an ROI may be set to be a consistent value, regardless of depth estimates that may have been assigned from other sources. Thereafter, other elements of image content may be assigned content adjustment values in gradients based on their relative depth in image content as compared to the base depth and, optionally, based on their spatial distance from the designated ROI. Image content may be adjusted based on the content adjustment values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application benefits from priority of Application No. 62/384,029,filed Sep. 6, 2016, and entitled “Use of Facial Landmarks in Depth ofField Applications,” the disclosure of which is incorporated herein byits entirety

BACKGROUND

Many modern consumer electronics products have the capability to captureand process image data. For example, laptop computers, tablet computers,smartphones and personal media devices may have cameras to capture imagedata and image editing applications that process the image data. Imageediting applications may provide tools to crop and/or rotate imagecontent and also to alter image content, for example, by altering imagebrightness, color content, sharpness and the like.

Some image editing applications attempt to alter image characteristicsautonomously thereby relieving human operators from the burden ofselecting and applying image editing tools. One such automated operationinvolves filtering. An image editing application may attempt to identifywhich portions are to be filtered based on assessments of the portions'characteristics. Automated analysis tools, however, sometimes develop“false positives.” For example, an analysis tool may assign a firstportion of an image for relatively heavy filtering but a second portionof an image for little or no filtering. When these filtered andunfiltered portions actually belong to a common element of an image,such as a human face, these differences in filtering lead to undesirableartifacts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an imaging system according to anembodiment of the present disclosure.

FIG. 2 illustrates a method according to an embodiment of the presentdisclosure.

FIG. 3(A) illustrates an exemplary input image on which embodiments ofthe present disclosure may be performed.

FIG. 3(B) illustrates operation of ROI identification according to anembodiment of the present disclosure.

FIG. 3(C) illustrates a depth map according to an embodiment of thepresent disclosure.

FIG. 3(D) illustrates a map of parameter strengths according to anembodiment of the present disclosure.

FIG. 3(E) is a graph illustrating exemplary parameter strengthsaccording to an embodiment of the present disclosure.

FIG. 4 illustrates a method according to another embodiment of thepresent disclosure.

FIGS. 5(A), 5(B) and 5(C) each illustrates an exemplary input image onwhich embodiments of the present disclosure may be performed.

FIG. 6 illustrates a method 600 according to another embodiment of thepresent disclosure.

FIG. 7(A) illustrates an exemplary input image on which embodiments ofthe present disclosure may be performed.

FIG. 7(B) is a graph illustrating idealized depth estimation of contentalong line b-b in FIG. 7(A).

FIG. 7(C) is a graph illustrating exemplary filter strengths that may beapplied according to an embodiment of the present disclosure.

FIG. 7(D) illustrates exemplary regions of interest generated accordingto an embodiment of the present disclosure.

FIG. 7(E) is a graph illustrating exemplary filter strengths that may beapplied according to an embodiment of the present disclosure.

FIGS. 8(A) and 8(B) each illustrate exemplary regions of interest thatmay be processed by embodiments of the present disclosure.

FIG. 9 illustrates an exemplary computer system suitable for use withembodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure provide techniques for automatedanalysis and filtering of image data. According to the embodiments,image data is analyzed to identify ROIs within the image content. Theimage data also may have depth estimates applied to content therein. Oneor more of the ROIs may be designated to possess a base depth,representing a depth of image content against which depths of othercontent may be compared. Moreover, the depth of the image content withina spatial area of an ROI may be set to be a consistent value, regardlessof depth estimates that may have been assigned from other sources.Thereafter, other elements of image content may be assigned contentadjustment values in gradients based on their relative depth in imagecontent as compared to the base depth and, optionally, based on theirspatial distance from the designated ROI.

The foregoing technique is expected to reduce the likelihood of falsepositives, which may occur if different elements of a common face, forexample, one eye or both, or a nose, is assigned a different depth thanother elements of the same face and are adjusted according to disparateparameters.

In an embodiment, parameter adjustments may be applied both for imagecontent that has shallower depth than the base depth and for imagecontent that has greater depth than the base depth.

In a further embodiment, multiple ROIs may be detected from image dataand the depths of each of the ROIs may be designated as respective basedepths. Content adjustment parameters may be assigned to other imagecontent based on differences between their depths and the base depths.

FIG. 1 is a block diagram of an imaging system 100 according to anembodiment of the present disclosure. The system 100 may include one ormore cameras 110 that capture image data representing a localenvironment; and an image processor 120 that performs processing onimage data provided by the camera(s) 110.

The camera(s) 110 may output image data as a sequence of frames. Theimage processor 120 may receive user input identifying a moment of imagecapture and may select frame(s) to be used as a captured imageresponsive to the user input. The user input also may identify a mode ofimage capture to be used, for example, still image capture, time-lapsecapture, stereoscope image capture, video capture, slow motion videocapture and the like, some of which may require the image processor 120to select a plurality of frames output from the camera(s) 110 ascaptured images.

The image processor 120 may output frames to other “image sink”components 130 within a device. For example, frames may be output to adisplay 132 or stored in memory 134. The frames may be output to a coder136 for compression and, ultimately, transmission to another device (notshown). The frames also may be consumed by an application 138, such asan image editor or a gaming application, that executes on the device.

The image processor 120 may perform content adjustment operations onselect images as discussed hereinbelow. For example, the framesassociated with image capture may be filtered, they may have brightnessadjusted, they may have their color saturation and/or hue adjusted,and/or they may be blended with content from other image(s). It is notrequired that an image processor 120 apply such filtering to all framesthat it receives or to all frames that it outputs to image sinks 130.For example, it may be convenient to avoid performing filteringoperations on frames that are output to a display 132 during imagecomposition operations that are performed before image capture. When theimage is composed and a user commands the device to perform imagecapture, the image processor 120 may perform its adjustment operationson the image(s) associated with the image capture event.

In an embodiment, the camera(s) 110 and image processor 120 may beprovided within a processing device 140, such as a smartphone, a tabletcomputer, a laptop computer, a desktop computer, a portable mediaplayer, or the like. The processing device 140 may have othercomponents, such as a user input device 150, which may be a touch screencontroller, control buttons, and the like.

FIG. 2 illustrates a method 200 according to an embodiment of thepresent disclosure. The method may identify a region of interest (ROI)from image content of a frame to be processed (box 210). The method 200may assign depths to image content within the frame (box 220). Themethod 200 may set a base depth at a depth associated with theidentified ROI (box 230). The method 200 may apply parameter gradientsfor other regions of the image content based on differences betweentheir respective depths and the base depth assigned to the ROI (box240). Finally, the method 200 may perform image adjustments according tothe parameter adjustments (box 250).

ROI identification (box 210) may occur in a variety of ways. In a firstembodiment, ROI identification may be performed based on facerecognition processes or body recognition processes applied to the imagecontent. ROI identification may be performed from an identification ofimages having predetermined coloration, for example, colors that arepreviously registered as corresponding to skin tones. Alternatively, ROIidentification may be performed based on relative movement of imagecontent across a temporally contiguous sequence of images. For example,content in a foreground of an image tends to exhibit larger overallmotion in image content than background content of the same image,whether due to movement of the object itself during image capture or dueto movement of a camera that performs the image capture.

Depth assignments (box 220) may occur in a variety of ways. In someembodiments, depth assignments may be made from analysis of imagecontent itself. For example, depth estimation may be performed based onrelative movement of image content across a temporally contiguoussequence of images. For example, content in a foreground of an imagetends to exhibit larger overall motion in image content than backgroundcontent of the same image, whether due to movement of the object itselfduring image capture or due to movement of a camera that performs theimage capture. Depth estimation also may be performed from an assessmentof an amount of blur in image content. For example, image content infocus may be identified as located at a depth corresponding to the focusrange of the camera that performs image capture whereas image contentthat is out of focus may be identified as being located at other depths.

In another embodiment involving a stereoscopic camera, depth assignmentsmay be made based on a disparity map generated from images output by thestereoscopic camera. For example, image content of a right-eye image maybe compared to content of a left-eye image and disparities may becalculated for each pixel location in the respective images. Thedisparities may represent a map from which depth values are estimated.

In other embodiments, depth assignments may be made from data sourcesoutside the image's content. When used with a camera having a movablelens system, depth assignments may be derived from lens positions thatare applied during auto-focus operations, which tends to correspond to adepth of foreground images from the camera. Depth assignments may bederived from a depth camera 160 (FIG. 1), for example, a structure lightor time-of-flight camera.

A base depth may be set (box 230) at a depth associated with a selectedROI. The base depth may represent a depth of image content that will begiven a base level of image adjustment (possibly none). Thus, when blurfiltering is applied, the ROI may be selected to have the sharpestcontent of any of the image content output by the method 200 followingfiltering, regardless of depth estimates that otherwise may have beenapplied to content within the ROI in box 220. Similarly, when brightnessadjustments are applied, the ROI may be selected to have the brightestcontent of any of the image content output by the method 200 followingfiltering, and other image content may be made darker. Further, whencolor saturation adjustments are applied, the ROI may be selected tohave the strongest color content of any of the image content output bythe method 200 following filtering, and other image content may be madeless saturated (e.g., more black and white). When color hue adjustmentsare applied, color content of the image may be altered based upon theirdepths (for example, blues may be converted to reds at various depths).Moreover, when blending adjustments are applied, blending weights may beassigned to content based upon their depths as compared to the basedepth; the blending weights may determine relative strengths at whichcontent from another image may be blended with the content of the imagebeing adjusted. When multiple ROIs are present in image content,selection of the ROI to which the base depth is applied may be performedin a variety of ways, such as by user selection, by prioritization ofROIs based on object type (e.g., faces may have priority over othertypes of objects), depth estimates of the ROIs as determined in box 220,etc.

Parameter adjustments may be defined for other regions of the imagecontent (box 240) in a graduated fashion, using the ROI's base level ofadjustment as a reference. In an example where the ROI is given noblurring, blurring gradients may be defined that apply heavier levels ofblur to regions of image content based on their distance from the ROI.Thus, image content that falls outside an ROI but is adjacent to the ROImay be given a lighter level of blur than image content that is at afarther distance from the ROI. Thus, the method 200 may develop a mapthat assigns various levels of blur to across the spatial area of theimage.

Parameter adjustments also may be defined for the other regions based onthose regions' relevant depths as compared to the base depth. Forexample, when an ROI is selected from foreground content, backgroundcontent spatially adjacent to the ROI may be assigned adjustmentparameters based on its relative depth. If an image contained foregroundcontent at a shallow depth (the ROI), “mid-ground” content at anintermediate depth and background content at a large depth, themid-ground content may be assigned a parameter adjustment based on itsdepth, even if the mid-ground content is spatially farther from the ROIthan the background content.

And, of course, parameter adjustments may be assigned to the otherregions based on a blend of the regions' spatial distance from an ROIand its depth as compared with the ROI's base depth.

Additionally, gradient levels may be assigned at edge regions of an ROI,where it borders other image elements.

Image adjustments may be applied to an image (box 250) based on theassigned adjustment parameters levels. For example, blurring may beperformed by a blur filter, using settings that are assigned based onthe parameters assigned in box 240. Similarly, brightness adjustments,color saturation/hue adjustments, blending adjustments, and the like,may be applied based on parameter levels that are assigned in box 240.

FIG. 3 illustrates operation of the method 200 of FIG. 2 on exemplaryimage data. In this example, FIG. 3(A) illustrates an exemplary inputimage on which the method 200 may operate. FIG. 3(B) illustratesoperation of ROI identification, which in this example, occurs byperforming face detection. FIG. 3(C) illustrates a depth maprepresenting depth estimation applied to the input image data. FIG. 3(D)illustrates a map representing image adjustment parameters to be appliedto image data. FIG. 3(E) is a graph illustrating exemplary weights thatmay be assigned to image data along the line e-e in FIG. 3(D).

As illustrated in FIG. 3(B), the face detection may detect predeterminedfacial “landmarks” from within image content that represent features ofthe human face. For example, the face detection may identify contentrepresenting eyes (represented by data points 310, 315), a nose 320,mouth 325, eyebrows 330, 335 and the profile of a head within imagedata. The face detection also may measure characteristics of thesefacial characteristics to determine their size in the image data. Themethod may identify an ROI from image content, shown in FIG. 3(A).

As illustrated in FIG. 3(C), the depth estimation may distinguishforeground from background elements in the image data. The depthestimation, however, may assign different depths to different foregroundelements in the depth map. For example, elements 311, 312, 313 and 314,which correspond respectively to structures about the eyes, nose, mouthand cheek of the subject, are shown having different depths. Iffiltering parameters were applied solely on the basis of depthassignments, then these elements 311-314 may be assigned differentfiltering strengths than other portions of the face data, which couldintroduce filtering artifacts in the resultant image data.

As discussed, FIG. 3(D) illustrates image adjustment parameters that maybe applied to image data. In FIG. 3(C), a dark color represents asmallest level of adjustment to be applied to data and the lightercolors represent heavier amounts of adjustment to be applied to imagedata. In an embodiment, the image content associated with an ROI may begiven a lightest adjustment setting (no adjustment, in an embodiment),regardless of the depth assignments made to content therein. Thereafter,parameter gradients may be assigned that provide increasing levels ofadjustment to image content as the depth differs from the depth of theROI content. A graph of exemplary weights is shown in FIG. 3(E).

FIG. 4 illustrates a method 400 according to another embodiment of thepresent disclosure. According to the embodiment, the method 400 mayassign depths to input content (box 410). The method 400 may identifyROI(s) from the input image (box 420). The method 400 may determinewhether a user selection was received (box 430). If a user selection wasreceived, the method 400 may determine whether an ROI is found within aregion of the image data associated with the user input (box 440). If anROI is found within a region of the image data associated with the userinput, the method 400 may assign a base depth to the selected ROIaccording to a depth of image content within the ROI in the region ofthe user comment (box 450). All content within the selected ROI may beassigned a common depth value as the base depth.

If no user selection was received (box 430) or if no ROI is found withinthe region of the user selection (box 440), the method 400 may set abase depth according to an ROI that is selected by a default technique,such as by content depth, object type classification and the like (box460). Thereafter, the method 400 may apply parameter adjustments toimage content at depths other than the base depth (box 470) and it mayperform image adjustments according to the parameter assignment in box470 (box 480).

In an embodiment, non-selected ROIs may have constrained depths assignedto them (box 490). That is, image content of an unselected ROI may havea common depth assigned to them regardless of depths that may have beenassigned to the ROI content at box 410. In this manner, when parameteradjustments are assigned at box 470, the image content of thenon-selected ROIs may have a uniform level of adjustment applied acrossthe ROI content. And, because the ROIs are non-selected, duringoperation of box 470, they may have a level of image adjustment assignedto them based on a difference between the non-selected ROI's depth andthe base depth of the ROI.

Depth assignments (box 410), ROI identification (box 420), base depthassignments (box 450, 460), parameter gradient assignments (box 470) andimage adjustments (box 480), may be performed according to any of thetechniques described hereinabove in connection with FIG. 2.

FIG. 5 illustrates processing of exemplary image data according to themethod of FIG. 4. FIG. 5(A) illustrates an exemplary input image fromwhich two ROIs are identified, ROI1 and ROI2. FIG. 5(A) illustrates userinput (a touch to focus command, or “TTF”) being entered on the screenin an area proximate to ROI2. Thus, the method may identify ROI2 ashaving the data to be assigned to the base depth (FIG. 5(B)).

Parameter adjustments may be assigned to a remainder of the image dataas their respective depths differ from the base depth of ROI2 (FIG.5(C)). In this manner, the image content of ROI1 and other image contentmay be subject to image adjustment of various degrees.

Note that the image content of ROI1 likely possesses a shallower depththan the image content of ROI2. In the absence of a touch to focuscommand, the base depth may have been set at the depth of image contentof ROI1 if the default selection process prioritized content depth overother factors. In this circumstance, other image content, including thatof ROI2, would have been adjusted as their depths differ from that ofROI1. Thus, the embodiment of FIG. 4 provides user control overassignments of base depth within image content.

Operation of box 490 (FIG. 4) may cause a non-selected ROI—here, ROI1—tobe assigned a common depth across the spatial area occupied by the ROI.In this manner, if depth assignments in box 410 (FIG. 4) causeddifferent portions of ROI1 to be assigned markedly different depths, forexample, some portions having a relatively shallow depth, other portionshaving a depth proximate to the depth of ROI2 and still other portionshaving a depth beyond ROI2, different levels of image adjustment mayhave been assigned across ROI1. When a common depth is assigned to theimage data of ROI1, it may cause a uniform level of filtering to beapplied to ROI1 based on a difference in depth between ROI1 and theselected ROI (ROI2).

FIG. 6 illustrates a method 600 according to another embodiment of thepresent disclosure. According to the embodiment, the method 600 mayassign depths to image data (box 610). The method 600 may identifyROI(s) from the input image (box 615), and may determine whethermultiple ROIs were detected (box 620). If multiple ROIs were detected,the method 600 may set a plurality of base depths, each at the depthsrespectively assigned to the ROIs (box 625). If multiple ROIs are notdetected, the method 600 may set a base depth according to a depth ofthe ROI (box 630). The method 600 may apply parameter adjustments toother image content based on differences in depth between the othercontent and the base depth(s) assigned either in box 625 or in box 630.Thereafter, the method 600 may perform image adjustment according to theparameters assigned in box 635 (box 640).

In another embodiment, when multiple base depths are assigned (box 625),the method 600 may grow a bounding region around the ROIs (box 645).Thereafter, the method 600 may apply parameter adjustments inside thebounding region at a first rate (box 655), and it may apply parameteradjustments outside the bounding region at a second rate (box 655). Themethod 600 may advance to box 640 and perform image adjustment accordingto the parameter levels assigned.

Depth assignments (box 610), ROI identification (box 615), base depthassignments (box 625, 630), and image adjustments (box 640), may beperformed according to any of the techniques described hereinabove inconnection with FIG. 2.

FIG. 7 illustrates processing of exemplary image data according to themethod of FIG. 6. FIG. 7(A) illustrates an exemplary input image fromwhich two ROIs are identified, ROI1 and ROI2. FIG. 7(B) is a graphillustrating idealized depth estimation of content along line b-b inFIG. 7(A). In practice, depth estimation data likely will includesignificant noise artifacts from the false positives discussed above;such artifacts are not illustrated in FIG. 7(B).

FIG. 7(C) illustrates an exemplary set of blur filter settings that maybe assigned to image content along the b-b line in FIG. 7(A). Asillustrated, operation of boxes 625 and 630 (FIG. the data of the ROI1and ROI2 may be assigned the smallest level of adjustment of all data inthe image and data of other image regions may be assigned adjustmentparameter settings according to a difference between their depth and thedepth of the ROIs. The data of the ROIs may be assigned a common levelof adjustment notwithstanding differences in their depth as illustratedin FIG. 7(B).

FIG. 7(D) illustrates an exemplary bounding region BR generated thatconnects the ROIs. During operation of boxes 645-655 image data withinthe bounding region BR may be assigned a first level of parameteradjustments according to their depth whereas image data outside thebounding region may be assigned a second level of parameter adjustments.The gradients may be applied as image data depth deviates from the basedepths SD1, SD2 assigned to the ROIs. In this manner, regions of imagedata in a spatial area between ROI1 and ROI2 may be given relativelylower levels of adjustment than background image data that is fartherremoved from either of the ROIs, even if the background image data hassimilar depth values to the image data within the bounding region.

In another embodiment (not shown), depths of multiple ROIs may be set toa single, common base depth, regardless of the depths that are assignedto image content of the ROI from image analysis. Consider the ROIs ofFIG. 7(A), for example, which have depth estimates assigned as shown inFIG. 7(B) from image analysis. ROI1 and ROI2 may be assigned a single,common depth, which is set to the base depth of the image. For example,although image analysis of ROI2 may cause its image data to be assigneda depth that is larger than the depth of ROI1, the depth of ROI2 may bereset to be equal to the depth of ROI1 and this common depth value maybe assigned as the base depth. Thereafter, parameter adjustments may beassigned to other image data based on differences in depth between theimage data and the single, base depth of the image. In this manner,gradients may be assigned with reference to a single base depth, ratherthan multiple base depths as discussed in some of the foregoingembodiments.

The foregoing discussion has described image adjustments as techniquesthat are to be applied at increasing levels to image content at depthsdifferent than those of the base depth. For example, the discussion hasassumed that no filtering, no brightness alteration, no color alterationand/or no blending would occur for image content at a base depth butsuch adjustments would be applied at increasing levels at locationsand/or depths that differ from the base depth. Such discussion is merelyexemplary. Other embodiments permit image adjustments to be applied attheir strongest level for image content at the base depth, then toreduce levels at other depths and/or other locations. For example,brightness enhancement might be applied at a base depth at a strongestlevel, then enhancement levels might be lowered at other depths and/orother locations. Alternatively, a strongest level of blending may beapplied to content of an ROI to achieve a “masking” effect; ROI datamight be replaced with alternative content from an external source tomask ROI content from the image, and blendings to other content may beapplied at reduced level(s) based on their estimated depths.

The principles of the present disclosure are not limited to the specificexamples discussed above. In addition to blurring, brightnessalteration, color adjustments and/or blending, image adjustmenttechniques can include spatial upsampling or downsampling, and the like.And, of course, the principles of the present disclosure permitapplication of multiple filters on a single image, each with their ownselections of parameter gradient assigned based on content depth and/orlocation with respect to and ROI and its base depth.

In an embodiment, parameter gradients may be assigned based on one ormore characteristics of an ROI. For example, where ROI detection isperformed based on face detection, different gradients may be assignedif the face is detected as fully present in image content (e.g., theface faces the camera) or if the face is detected as partially-presentin image content (e.g., the face is in full or partial profile). Inanother instance, parameter gradients may be applied based on selectedcharacteristics of the detected ROI (e.g., eye-nose separation distance,face size, and the like).

In some instances, gradients may be applied differently based on anorientation of an ROI. For example, as illustrated in FIGS. 8(A) and8(B), when ROI identification is based on face detection, the ROIidentification process also may detect an orientation of the ROI inimage content. In the example of FIG. 8(A), a detected face isidentified as having a long axis that is aligned to a vertical directionof image content. In FIG. 8(B), however, the detected face has a longaxis that is rotated with respect to a vertical direction by an angle θ.Gradients may be applied in a directionally-specific manner along theROI based on its orientation to alter amounts of image adjustment thatwill be applied.

In the examples illustrated in FIG. 8(A) and FIG. 8(B), gradients may beapplied differently along the long axis of the ROI. For example,gradients may be applied to reduce an amount of image adjustment along aportion of the axis corresponding to a lower part of the face. In thisexample, such management of gradients may provide better adjustmentresults when an ROI represents a face that has a beard, for example.

In an embodiment, the techniques described herein may be performed by acentral processor of a computer system. FIG. 9 illustrates an exemplarycomputer system 900 that may perform such techniques. The computersystem 900 may include a central processor 910, one or more cameras 920,and a memory 930 provided in communication with one another. The camera920 may perform image capture and may store captured image data in thememory 930. Optionally, the device also may include sink components,such as a display 940 and a coder 950, as desired.

The central processor 910 may read and execute various programinstructions stored in the memory 930 that define an operating system912 of the system 900 and various applications 914.1-914.N. The programinstructions may perform image filtering according to the techniquesdescribed herein. As it executes those program instructions, the centralprocessor 910 may read, from the memory 930, image data created by thecamera 920, and it may perform ROI detection, depth estimation, andfiltering as described hereinabove.

As indicated, the memory 930 may store program instructions that, whenexecuted, cause the processor to perform the techniques describedhereinabove. The memory 930 may store the program instructions onelectrical-, magnetic- and/or optically-based storage media.

The image processor 120 (FIG. 1) and the central processor 910 (FIG. 9)may be provided in a variety of implementations. They can be embodied inintegrated circuits, such as application specific integrated circuits,field programmable gate arrays, digital signal processors and/or generalpurpose processors.

Several embodiments of the disclosure are specifically illustratedand/or described herein. However, it will be appreciated that theteachings of this the disclosure may find application in otherimplementations without departing from the spirit and intended scope ofthe disclosure.

We claim:
 1. A method, comprising: performing depth estimation on animage, the depth estimation generating an initial depth map having depthvalues for content elements of the image; identifying a region ofinterest within the image; altering, within the depth map, depth valuesof a plurality of content elements within the region of interest frominitially-different depth values to a common value of a base depth;assigning adjustment parameter strengths to the content elements of theimage based on a comparison of depths of the content elements of theimage and the base depth; and adjusting the image according to theassigned parameter strengths.
 2. The method of claim 1, whereinadjustment parameter strengths are assigned further based on acomparison of an image location of each content element and an imagelocation of the region of interest.
 3. The method of claim 1, whereinthe adjusting is blur filtering, and content at the base depth has alowest-strength filtering for the image.
 4. A method, comprising:performing depth estimation on an image; identifying a region ofinterest within the image; assigning a base depth to content of theimage within the region of interest; assigning adjustment parameterstrengths to content of the image based on a comparison of depths of theimage content and the base depth of the region of interest, wherein anedge portion of the region of interest is assigned a parameter strengththat is representing a gradient level; and adjusting the image accordingto the assigned parameter strengths.
 5. A method, comprising: performingdepth estimation on an image; identifying a region of interest withinthe image; assigning a base depth to content of the image within theregion of interest; assigning adjustment parameter strengths to contentof the image based on a comparison of depths of the image content andthe base depth of the region of interest, wherein different portions ofthe region of interest are assigned different parameter strengths basedon orientation of the different portions of the region of interest; andadjusting the image according to the assigned parameter strengths. 6.The method of claim 1, wherein the adjusting is brightness adjustment.7. The method of claim 1, wherein the adjusting is color saturationadjustment.
 8. The method of claim 1, wherein the adjusting is color hueadjustment.
 9. The method of claim 1, wherein the adjusting is ablending of another image with the image.
 10. The method of claim 1,wherein the depth estimation is derived from analysis of image content.11. The method of claim 1, wherein, the identifying comprisesidentifying multiple regions of interest, and the altering comprisesaltering depth values of content elements that correspond to themultiple regions of interest to a common base depth.
 12. The method ofclaim 1, wherein, the identifying comprises identifying multiple regionsof interest, and, the altering comprises altering depth values ofcontent elements that correspond to each of the multiple regions ofinterest to a respective base depth, and the assigning comprisesassigning adjustment parameter strengths to the content elements of theimage based on a comparison of depths of the content elements of theimage and the respective base depths.
 13. The method of claim 1,wherein, the identifying comprises identifying multiple regions ofinterest, and a base depth is assigned to one of the multiple regions ofinterest according to a selection protocol.
 14. The method of claim 13,wherein the selection protocol selects the one region of interest inresponse to user input.
 15. The method of claim 13, wherein theselection protocol selects the one region of interest based on priorityamong types of regions of interest.
 16. The method of claim 13, whereinthe selection protocol selects the one region of interest based on apriority among content depth within the regions of interest determinedby the depth estimation.
 17. The method of claim 1, wherein the regionof interest is identified by face detection.
 18. The method of claim 17,wherein the parameter strengths are assigned as gradients along an axisof a detected face.
 19. A device, comprising: a camera, and an imageprocessor to: perform depth estimation on an image output by the camera,the depth estimation generating an initial depth map having depth valuesfor content elements of the image; identify a region of interest withinthe image; alter, within the depth map, depth values of a plurality ofcontent elements within the region of interest from initially-differentdepth values to a common value of a base depth; assign parameterstrengths to the content elements of the image based on a comparison ofdepths of the content elements of the image and the base depth; andadjust the image according to the assigned parameter strengths.
 20. Thedevice of claim 19, wherein the image processor: identifies multipleregions of interest, and assigns adjustment parameter strengths based ona comparison of an image location of each content element and an imagelocation of a region of interest of the multiple regions of interest.21. The method of claim 1, wherein the adjusting is blur filtering. 22.A device, comprising: a camera, and an image processor to: perform depthestimation on an image output by the camera, identify a region ofinterest within the image; assign a base depth to content of the imagewithin the region of interest, assign parameter strengths to content ofthe image based on a comparison of depths of the image content and thebase depth, wherein an edge portion of the region of interest isassigned a parameter strength that is representing a gradient level; andadjust the image according to the assigned parameter strengths.
 23. Adevice, comprising: a camera, and an image processor to: perform depthestimation on an image output by the camera, identify a region ofinterest within the image; assign a base depth to content of the imagewithin the region of interest, assign parameter strengths to content ofthe image based on a comparison of depths of the image content and thebase depth, wherein different portions of the region of interest areassigned different parameter strengths based on orientation of thedifferent portions of the region of interest; and adjust the imageaccording to the assigned parameter strengths.
 24. The device of claim19, wherein, the image processor: identifies multiple regions ofinterest, and alters, within the depth map, depth values of contentelements that correspond to the multiple regions of interest to a commonbase depth.
 25. The device of claim 19, wherein, the image processor:identifies multiple regions of interest, alters depth values of contentelements that correspond to each of the regions of interest to arespective base depth, and assigns adjustment parameter strengths to thecontent elements of the image based on a comparison of depths of thecontent elements of the image and the base depths.
 26. The device ofclaim 19, wherein, the image processor: identifies multiple regions ofinterest, and assigns a base depth to one of the regions of interestaccording to a selection protocol.
 27. The device of claim 26, furthercomprising a user input device, wherein the selection protocol selectsthe one region of interest in response to user input.
 28. The device ofclaim 26, wherein the selection protocol selects the one region ofinterest based on priority among types of regions of interest.
 29. Thedevice of claim 26, wherein the selection protocol selects the oneregion of interest based on a priority among content depth within theregions of interest determined by the depth estimation.
 30. Anon-transitory computer readable storage medium storing programinstructions that, when executed by a processing device, causes thedevice to: perform depth estimation on an image, the depth estimationgenerating an initial depth map having depth values for content elementsof the image; identify a region of interest within the image; alter,within the depth map, depth values of a plurality of content elementswithin the region of interest from initially-different depth values to acommon value of a base depth; assign adjustment parameter strengths tothe content elements of the image based on a comparison of depths of thecontent elements of the image and the base depth; and filter the imageaccording to the assigned adjustment parameter strengths.
 31. Anon-transitory computer readable storage medium storing programinstructions that, when executed by a processing device, causes thedevice to: perform depth estimation on an image; identify a region ofinterest within the image; assign a base depth to content of the imagewithin the region of interest; assign adjustment parameter strengths tocontent of the image based on a comparison of depths of the imagecontent and the base depth, wherein an edge portion of the region ofinterest is assigned a parameter strength that is representing agradient level; and filter the image according to the assignedadjustment parameter strengths.
 32. The storage medium of claim 30,wherein, the identifying comprises identifying multiple regions ofinterest, and the program instructions cause the processing device toalter depth values of content elements that correspond to the multipleregions of interest to a common base depth.
 33. The storage medium ofclaim 30, wherein, the identifying comprises identifying multipleregions of interest, and the program instructions cause the processingdevice to: alter depth values of content elements that correspond toeach of the regions of interest to a respective base depth, and assignadjustment parameter strengths to the content elements of the imagebased on a comparison of depths of the content elements of the image andthe base depths.
 34. The storage medium of claim 30, wherein, theidentifying comprises identifying multiple regions of interest, and theprogram instructions cause the processing device to assign a base depthto one of the regions of interest according to a selection protocol. 35.A non-transitory computer readable storage medium storing programinstructions that, when executed by a processing device, causes thedevice to: perform depth estimation on an image; identify a region ofinterest within the image; assign a base depth to content of the imagewithin the region of interest; assign adjustment parameter strengths tocontent of the image based on a comparison of depths of the imagecontent and the base depth, wherein different portions of the region ofinterest are assigned different parameter strengths based on orientationof the different portions of the region of interest; and filter theimage according to the assigned adjustment parameter strengths.
 36. Themethod of claim 11, wherein the assigning adjustment parameter strengthscomprises: applying adjustment parameter strengths inside a boundingregion at a first rate and applying adjustment parameter strengthsoutside the bounding region at a second rate, wherein the boundingregion is an image region that contains at least two regions of interestof the multiple regions of interest.
 37. The method of claim 12, whereinthe assigning adjustment parameter strengths comprises: applyingadjustment parameter strengths inside a bounding region at a first rateand applying adjustment parameter strengths outside the bounding regionat a second rate, wherein the bounding region is an image region thatcontains at least two regions of interest of the multiple regions ofinterest.
 38. The method of claim 1, wherein the region of interestcomprises content elements of the image that belong to an object.